Natural and Anthropogenic Variation of Stand Structure and Aboveground Biomass in Niger Delta Mangrove Forests

Abstract

Mangrove forests are important coastal wetlands because of the ecosystem services they provide especially their carbon potential. Mangrove forests productivity in the Niger Delta are poorly quantified and at risk of loss from oil pollution, deforestation, and invasive species. Here, we report the most extensive stem girth survey yet of mangrove plots for stand and canopy structure in the Niger Delta, across tidal and disturbance gradients. We established twenty-five geo-referenced 0.25-ha plots across two estuarine basins. We estimated aboveground biomass (AGB) from established allometric equations based on stem surveys. Leaf area index (LAI) was recorded using hemispherical photos. We estimated a mean AGB of 83.7 Mg ha–1 with an order of magnitude range, from 11 to 241 Mg ha–1. We found significantly higher plot biomass in close proximity to a protected site and tidal channels, and the lowest in the sites where urbanization and wood exploitation was actively taking place. The mean LAI was 1.45 and ranged fivefold from 0.46 to 2.41 and there was a significant positive correlation between AGB and LAI (R2 = 0.31). We divided the plots into two disturbance regimes and three nipa palm (Nypa fruticans) invasion levels. Lower stem diameter (5–15 cm) accounted for 70% of the total biomass in disturbed plots, while undisturbed regimes had a more even (∼25%) contribution of different diameter at breast height (DBH) size classes to AGB. Nipa palm invasion also showed a significant link to larger variations in LAI and the proportion of basal area removed from plots. We conclude that mangrove forest degradation and exploitation is removing larger stems (>15 cm DBH), preferentially from these mangroves forests and creates an avenue for nipa palm colonization. This research identifies opportunities to manage the utilization of mangrove resources and reduce any negative impact. Our data can be used with remote sensing to estimate biomass in the Niger Delta and the inclusion of soil, leaf properties and demographic rates can analyze mangrove-nipa competition in the region.

Response Mechanism of Leaf Area Index and Main Nutrient Content in Mangrove Supported by Hyperspectral Data

“…LAI represents the density of vegetation leaves, and is the key factor affecting the photosynthetic effective radiation capacity of the canopy in the carbon cycle. Its level directly affects the strength of the photosynthetic capacity, and has an important impact on the global carbon cycle and vegetation growth and development [37,46,47]. Accurately grasping the mangrove LAI represents the basic work undertaken to evaluate the vegetation growth status in the land and sea transition zone.…”

“…The determination principle is that they have the maximum absorption values at 645 nm and 663 nm. The content value can be calculated according to the empirical formula; total nitrogen was determined by UNICUBE trace element analysis; total phosphorus and total potassium were determined by Axios-type X-ray fluorescence spectrometry [46]. Note: The unit of LAI is m 2 m −2 ; the unit of MSTR is %; the unit of Chla, Chlb, TN, TP, and TK is g kg −1 .…”

“…The research shows that the combined use of data and methods is helpful for the estimation of mangrove biomass [44]. The relative amounts of morphology, forest age, canopy coverage, aboveground biomass, and wood debris were extracted from the time series data of spaceborne optical radar and interferometric radar data [45,46]. The best method to interpret the change in mangrove carbon storage using remote sensing data was found through image processing [47,48].…”

“…However, there is some uncertainty in the application of quantitative inversion of LAI due to the wide and discontinuous band of multispectral remote sensing data [1]. Hyperspectral data can provide rich and detailed continuous spectral band information [37,46]. With the continuous development of hyperspectral remote sensing technology, a large number of researchers began to retrieve LAI, chlorophyll, and other plant physiological parameters based on hyperspectral remote sensing methods [40,47].…”

Hierarchical Bayesian geostatistics for C stock prediction in disturbed plantation forest in Zimbabwe

“…The plausibility of distance to nearest settlements as a predictor of C stock rationalises from the circumstances prevailing in the plantation forests after the agrarian land reform of 2000. Secondly, studies undertaken elsewhere, including Chinembiri et al (2013) and Nwobi and Williams (2021), have shown distance to settlements as a significant predictor of C stock in perturbed and related environments.…”